Contrastive learning [1]}, [2]}, [3]}, [4]}, [5]}
techniques seek to learn good representations \(\Phi \colon \mathcal {X}\rightarrow \mathbb {R}^K\) 
by aligning similar instances (e.g., a sample and its perturbation),
and pushing apart dissimilar instances (e.g., pairs of random samples).
This may be achieved by minimising \(\Omega _{\rm con}( x ) \stackrel{.}{=}\)
\( \mathbb {E}_{x^+, \mathcal {N}( x )} \log \left[ 1 + \sum \nolimits _{x^-\in \mathcal {N}( x )} e^{\Phi ( x )^\top \Phi ( x^-) - \Phi ( x )^\top \Phi ( x^+)} \right], \) 
